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Buckler AJ. Invited Commentary: Focus on Quantitative Imaging—Real Progress Is Being Made, but Much Is Left to Do. Radiographics 2019; 39:977-980. [DOI: 10.1148/rg.2019190063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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2
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MacNee W. Computed tomography-derived pathological phenotypes in COPD. Eur Respir J 2018; 48:10-3. [PMID: 27365503 DOI: 10.1183/13993003.00958-2016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 05/16/2016] [Indexed: 11/05/2022]
Affiliation(s)
- William MacNee
- University of Edinburgh/MRC Centre for Inflammation Research, Queen's Medical Research Institute, Edinburgh, UK
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Tanabe N, Vasilescu DM, McDonough JE, Kinose D, Suzuki M, Cooper JD, Paré PD, Hogg JC. Micro-Computed Tomography Comparison of Preterminal Bronchioles in Centrilobular and Panlobular Emphysema. Am J Respir Crit Care Med 2017; 195:630-638. [PMID: 27611890 DOI: 10.1164/rccm.201602-0278oc] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
RATIONALE Very little is known about airways that are too small to be visible on thoracic multidetector computed tomography but larger than the terminal bronchioles. OBJECTIVES To examine the structure of preterminal bronchioles located one generation proximal to terminal bronchioles in centrilobular and panlobular emphysema. METHODS Preterminal bronchioles were identified by backtracking from the terminal bronchioles, and their centerlines were established along the entire length of their lumens. Multiple cross-sectional images perpendicular to the centerline were reconstructed to evaluate the bronchiolar wall and lumen, and the alveolar attachments to the outer airway walls in relation to emphysematous destruction in 28 lung samples from six patients with centrilobular emphysema, 20 lung samples from seven patients with panlobular emphysema associated with alpha-1 antitrypsin deficiency, and 47 samples from seven control (donor) lungs. MEASUREMENTS AND MAIN RESULTS The preterminal bronchiolar length, wall volume, total volume (wall + lumen), lumen circularity, and number of alveolar attachments were reduced in both centrilobular and panlobular emphysema compared with control lungs. In contrast, thickening of the wall and narrowing of the lumen were more severe and heterogeneous in centrilobular than in panlobular emphysema. The bronchiolar lumen was narrower in the middle than at both ends, and the decreased number of alveolar attachments was associated with increased wall thickness in centrilobular emphysema. CONCLUSIONS These results provide new information about small airways pathology in centrilobular and panlobular emphysema and show that these changes affect airways that are not visible with thoracic multidetector computed tomography scans but located proximal to the terminal bronchioles in chronic obstructive pulmonary disease.
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Affiliation(s)
- Naoya Tanabe
- 1 Centre for Heart and Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Dragoş M Vasilescu
- 1 Centre for Heart and Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - John E McDonough
- 1 Centre for Heart and Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada.,2 Department of Clinical and Experimental Medicine, Division of Respiratory Diseases, KU Leuven-University of Leuven, Leuven, Belgium
| | - Daisuke Kinose
- 1 Centre for Heart and Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Masaru Suzuki
- 1 Centre for Heart and Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada.,3 First Department of Medicine, Hokkaido University School of Medicine, Sapporo, Japan; and
| | - Joel D Cooper
- 4 Division of Thoracic Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter D Paré
- 1 Centre for Heart and Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - James C Hogg
- 1 Centre for Heart and Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
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Pompe E, van Rikxoort EM, Mets OM, Charbonnier JP, Kuhnigk JM, de Koning HJ, Oudkerk M, Vliegenthart R, Zanen P, Lammers JWJ, van Ginneken B, de Jong PA, Mohamed Hoesein FAA. Follow-up of CT-derived airway wall thickness: Correcting for changes in inspiration level improves reliability. Eur J Radiol 2016; 85:2008-2013. [PMID: 27776653 DOI: 10.1016/j.ejrad.2016.09.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 09/09/2016] [Accepted: 09/12/2016] [Indexed: 10/21/2022]
Abstract
OBJECTIVES Airway wall thickness (AWT) is affected by changes in lung volume. This study evaluated whether correcting AWT on computed tomography (CT) for differences in inspiration level improves measurement agreement, reliability, and power to detect changes over time. METHODS Participants of the Dutch-Belgian lung cancer screening trial who underwent 3-month repeat CT for an indeterminate pulmonary nodule were included. AWT on CT was calculated by the square root of the wall area at a theoretical airway with an internal perimeter of 10mm (Pi10). The scan with the highest lung volume was labelled as the reference scan and the scan with the lowest lung volume was labelled as the comparison scan. Pi10 derived from the comparison scan was corrected by multiplying it with the ratio of CT lung volume of the comparison scan to CT lung volume on the reference scan. Agreement of uncorrected and corrected Pi10 was studied with the Bland-Altman method, reliability with intra-class correlation coefficients (ICC), and power to detect changes over time was calculated. RESULTS 315 male participants were included. Limit of agreement and reliability for Pi10 was -0.61 to 0.57mm (ICC=0.87), which improved to -0.38 to 0.37mm (ICC=0.94) after correction for inspiration level. To detect a 15% change over 3 months, 71 subjects are needed for Pi10 and 26 subjects for Pi10 adjusted for inspiration level. CONCLUSIONS Correcting Pi10 for differences in inspiration level improves reliability, agreement, and power to detect changes over time.
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Affiliation(s)
- Esther Pompe
- Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Eva M van Rikxoort
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Onno M Mets
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jean-Paul Charbonnier
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan-Martin Kuhnigk
- Institute for Medical Image Computing, Fraunhofer MEVIS, Bremen, Germany
| | - Harry J de Koning
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Matthijs Oudkerk
- University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, The Netherlands
| | - Rozemarijn Vliegenthart
- University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, The Netherlands; University of Groningen, University Medical Center Groningen, Center for Medical Imaging-North East Netherlands, Groningen, The Netherlands
| | - Pieter Zanen
- Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jan-Willem J Lammers
- Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bram van Ginneken
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
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Hoffman EA, Lynch DA, Barr RG, van Beek EJR, Parraga G. Pulmonary CT and MRI phenotypes that help explain chronic pulmonary obstruction disease pathophysiology and outcomes. J Magn Reson Imaging 2016; 43:544-57. [PMID: 26199216 PMCID: PMC5207206 DOI: 10.1002/jmri.25010] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 07/01/2015] [Indexed: 12/12/2022] Open
Abstract
Pulmonary x-ray computed tomographic (CT) and magnetic resonance imaging (MRI) research and development has been motivated, in part, by the quest to subphenotype common chronic lung diseases such as chronic obstructive pulmonary disease (COPD). For thoracic CT and MRI, the main COPD research tools, disease biomarkers are being validated that go beyond anatomy and structure to include pulmonary functional measurements such as regional ventilation, perfusion, and inflammation. In addition, there has also been a drive to improve spatial and contrast resolution while at the same time reducing or eliminating radiation exposure. Therefore, this review focuses on our evolving understanding of patient-relevant and clinically important COPD endpoints and how current and emerging MRI and CT tools and measurements may be exploited for their identification, quantification, and utilization. Since reviews of the imaging physics of pulmonary CT and MRI and reviews of other COPD imaging methods were previously published and well-summarized, we focus on the current clinical challenges in COPD and the potential of newly emerging MR and CT imaging measurements to address them. Here we summarize MRI and CT imaging methods and their clinical translation for generating reproducible and sensitive measurements of COPD related to pulmonary ventilation and perfusion as well as parenchyma morphology. The key clinical problems in COPD provide an important framework in which pulmonary imaging needs to rapidly move in order to address the staggering burden, costs, as well as the mortality and morbidity associated with COPD.
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Affiliation(s)
- Eric A Hoffman
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - David A Lynch
- Department of Radiology, National Jewish Health Center, Denver, Colorado, USA
| | - R Graham Barr
- Division of General Medicine, Division of Pulmonary, Allergy and Critical Care, Department of Medicine, Columbia University Medical Center, New York, New York, USA
- Department of Epidemiology, Columbia University Medical Center, New York, New York, USA
| | - Edwin J R van Beek
- Clinical Research Imaging Centre, Queen's Medical Research Institute, University of Edinburgh, Scotland, UK
| | - Grace Parraga
- Robarts Research Institute, University of Western Ontario, London, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Canada
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6
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Kirby M, Lane P, Coxson HO. Measurement of pulmonary structure and function. IMAGING 2016. [DOI: 10.1183/2312508x.10003415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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7
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Sullivan DC, Obuchowski NA, Kessler LG, Raunig DL, Gatsonis C, Huang EP, Kondratovich M, McShane LM, Reeves AP, Barboriak DP, Guimaraes AR, Wahl RL. Metrology Standards for Quantitative Imaging Biomarkers. Radiology 2015; 277:813-25. [PMID: 26267831 PMCID: PMC4666097 DOI: 10.1148/radiol.2015142202] [Citation(s) in RCA: 307] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Although investigators in the imaging community have been active in developing and evaluating quantitative imaging biomarkers (QIBs), the development and implementation of QIBs have been hampered by the inconsistent or incorrect use of terminology or methods for technical performance and statistical concepts. Technical performance is an assessment of how a test performs in reference objects or subjects under controlled conditions. In this article, some of the relevant statistical concepts are reviewed, methods that can be used for evaluating and comparing QIBs are described, and some of the technical performance issues related to imaging biomarkers are discussed. More consistent and correct use of terminology and study design principles will improve clinical research, advance regulatory science, and foster better care for patients who undergo imaging studies.
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Affiliation(s)
- Daniel C. Sullivan
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Nancy A. Obuchowski
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Larry G. Kessler
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - David L. Raunig
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Constantine Gatsonis
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Erich P. Huang
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Marina Kondratovich
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Lisa M. McShane
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Anthony P. Reeves
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Daniel P. Barboriak
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Alexander R. Guimaraes
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
| | - Richard L. Wahl
- From the Department of Radiology, Duke University Medical Center, Box 2715, Durham, NC 27710 (D.C.S., D.P.B.); Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Public Health, University of Washington, Seattle, Wash (L.G.K.); Department of Informatics, ICON Medical, Washington, Pa (D.L.R.); Center for Statistical Sciences, Brown University, Providence, RI (C.G.); National Cancer Institute, Bethesda, Md (E.P.H., L.M.M.); Center for Devices and Radiological Health, U.S. Food and Drug Administration, White Oak, Md (M.K.); Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY (A.P.R.); Department of Radiology, Oregon Health & Science University, Portland, Ore (A.R.G.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.)
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Boutou AK, Zoumot Z, Nair A, Davey C, Hansell DM, Jamurtas A, Polkey MI, Hopkinson NS. The Impact of Homogeneous Versus Heterogeneous Emphysema on Dynamic Hyperinflation in Patients With Severe COPD Assessed for Lung Volume Reduction. COPD 2015; 12:598-605. [PMID: 26398112 PMCID: PMC4776679 DOI: 10.3109/15412555.2015.1020149] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Dynamic hyperinflation (DH) is a pathophysiologic hallmark of Chronic Obstructive Pulmonary Disease (COPD). The aim of this study was to investigate the impact of emphysema distribution on DH during a maximal cardiopulmonary exercise test (CPET) in patients with severe COPD. This was a retrospective analysis of prospectively collected data among severe COPD patients who underwent thoracic high-resolution computed tomography, full lung function measurements and maximal CPET with inspiratory manouvers as assessment for a lung volume reduction procedure. ΔIC was calculated by subtracting the end-exercise inspiratory capacity (eIC) from resting IC (rIC) and expressed as a percentage of rIC (ΔIC %). Emphysema quantification was conducted at 3 predefined levels using the syngo PULMO-CT (Siemens AG); a difference >25% between best and worse slice was defined as heterogeneous emphysema. Fifty patients with heterogeneous (62.7% male; 60.9 ± 7.5 years old; FEV1% = 32.4 ± 11.4) and 14 with homogeneous emphysema (61.5% male; 62.5 ± 5.9 years old; FEV1% = 28.1 ± 10.3) fulfilled the enrolment criteria. The groups were matched for all baseline variables. ΔIC% was significantly higher in homogeneous emphysema (39.8% ± 9.8% vs.31.2% ± 13%, p = 0.031), while no other CPET parameter differed between the groups. Upper lobe predominance of emphysema correlated positively with peak oxygen pulse, peak oxygen uptake and peak respiratory rate, and negatively with ΔIC%. Homogeneous emphysema is associated with more DH during maximum exercise in COPD patients.
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Affiliation(s)
- Afroditi K Boutou
- a 1 NIHR Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College , London United Kingdom
| | - Zaid Zoumot
- a 1 NIHR Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College , London United Kingdom.,b 2 Respiratory and Critical Care Institute, Cleveland Clinic Abu Dhabi , Abu Dhabi , UAE
| | - Arjun Nair
- a 1 NIHR Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College , London United Kingdom
| | - Claire Davey
- a 1 NIHR Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College , London United Kingdom
| | - David M Hansell
- a 1 NIHR Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College , London United Kingdom
| | - Athanasios Jamurtas
- c 3 Department of Sports Education and Physical Science, University of Thessaly , Trikala , Greece
| | - Michael I Polkey
- a 1 NIHR Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College , London United Kingdom
| | - Nicholas S Hopkinson
- a 1 NIHR Biomedical Research Unit at Royal Brompton and Harefield NHS Foundation Trust and Imperial College , London United Kingdom
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9
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Larsson E, Tromba G, Uvdal K, Accardo A, Monego SD, Biffi S, Garrovo C, Lorenzon A, Dullin C. Quantification of structural alterations in lung disease—a proposed analysis methodology of CT scans of preclinical mouse models and patients. Biomed Phys Eng Express 2015. [DOI: 10.1088/2057-1976/1/3/035201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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10
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Pike D, Kirby M, Guo F, McCormack DG, Parraga G. Ventilation heterogeneity in ex-smokers without airflow limitation. Acad Radiol 2015; 22:1068-78. [PMID: 26008133 DOI: 10.1016/j.acra.2015.04.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Revised: 04/13/2015] [Accepted: 04/17/2015] [Indexed: 11/30/2022]
Abstract
RATIONALE AND OBJECTIVES Hyperpolarized (3)He magnetic resonance imaging (MRI) ventilation abnormalities are visible in ex-smokers without airflow limitation, but the clinical relevance of this is not well-understood. Our objective was to phenotype healthy ex-smokers with normal and abnormally elevated ventilation defect percent (VDP). MATERIALS AND METHODS Sixty ex-smokers without airflow limitation provided written informed consent to (3)He MRI, computed tomography (CT), and pulmonary function tests in a single visit. (3)He MRI VDP and apparent diffusion coefficients (ADCs) were measured for whole-lung and each lung lobe as were CT measurements of emphysema (relative area [RA] with attenuation ≤-950 HU, RA950) and airway morphology (wall area percent [WA%], lumen area [LA] and LA normalized to body surface area [LA/BSA]). RESULTS In 42 ex-smokers, there was abnormally elevated VDP and no significant differences for pulmonary function, RA950, or airway measurements compared to 18 ex-smokers with normal VDP. Ex-smokers with abnormally elevated VDP reported significantly greater (3)He ADC in the apical lung (right upper lobe [RUL], P = .02; right middle lobe [RML], P = .04; and left upper lobe [LUL], P = .009). Whole lung (r = 0.40, P = .001) and lobar VDP (RUL, r = 0.32, P = .01; RML, r = 0.46, P = .002; right lower lobe [RLL], r = 0.38, P = .003; LUL, r = 0.35, P = .006; and left lower lobe, r = 0.37, P = .004) correlated with regional (3)He ADC. Although whole-lung VDP and CT airway morphology measurements were not correlated, regional VDP was correlated with RUL LA (r = -0.37, P = .004), LA/BSA (r = -0.42, P = .0008), RLL WA% (r = 0.28, P = .03), LA (r = -0.28, P = .03), and LA/BSA (r = -0.37, P = .004). CONCLUSIONS Abnormally elevated VDP in ex-smokers without airflow limitation was coincident with very mild emphysema detected using MRI and regional airway remodeling detected using CT representing a subclinical obstructive lung disease phenotype.
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Affiliation(s)
- Damien Pike
- Imaging Research Laboratories, Robarts Research Institute, 1151 Richmond St N, London, ON, Canada N6A 5B7; Department of Medical Biophysics, The University of Western Ontario, London, Canada
| | - Miranda Kirby
- James Hogg Research Centre, St. Paul's Hospital, University of British Columbia, Vancouver, Canada
| | - Fumin Guo
- Imaging Research Laboratories, Robarts Research Institute, 1151 Richmond St N, London, ON, Canada N6A 5B7; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Canada
| | - David G McCormack
- Division of Respirology, Department of Medicine, The University of Western Ontario, London, Canada
| | - Grace Parraga
- Imaging Research Laboratories, Robarts Research Institute, 1151 Richmond St N, London, ON, Canada N6A 5B7; Department of Medical Biophysics, The University of Western Ontario, London, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Canada.
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Variation in the percent of emphysema-like lung in a healthy, nonsmoking multiethnic sample. The MESA lung study. Ann Am Thorac Soc 2015; 11:898-907. [PMID: 24983825 DOI: 10.1513/annalsats.201310-364oc] [Citation(s) in RCA: 98] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
RATIONALE Computed tomography (CT)-based lung density is used to quantitate the percentage of emphysema-like lung (hereafter referred to as percent emphysema), but information on its distribution among healthy nonsmokers is limited. OBJECTIVES We evaluated percent emphysema and total lung volume on CT scans of healthy never-smokers in a multiethnic, population-based study. METHODS The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study investigators acquired full-lung CT scans of 3,137 participants (ages 54-93 yr) between 2010-12. The CT scans were taken at full inspiration following the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) protocol. "Healthy never-smokers" were defined as participants without a history of tobacco smoking or respiratory symptoms and disease. "Percent emphysema" was defined as the percentage of lung voxels below -950 Hounsfield units. "Total lung volume" was defined by the volume of lung voxels. MEASUREMENTS AND MAIN RESULTS Among 854 healthy never-smokers, the median percent emphysema visualized on full-lung scans was 1.1% (interquartile range, 0.5-2.5%). The percent emphysema values were 1.2 percentage points higher among men compared with women and 0.7, 1.2, and 1.2 percentage points lower among African Americans, Hispanics, and Asians compared with whites, respectively (P < 0.001). Percent emphysema was positively related to age and height and inversely related to body mass index. The findings were similar for total lung volume on CT scans and for percent emphysema defined at -910 Hounsfield units and measured on cardiac scans. Reference equations to account for these differences are presented for never, former and current smokers. CONCLUSIONS Similar to lung function, percent emphysema varies substantially by demographic factors and body size among healthy never-smokers. The presented reference equations will assist in defining abnormal values for percent emphysema and total lung volume on CT scans, although validation is pending.
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The volume ratio of ground glass opacity in early lung CT predicts mortality in acute paraquat poisoning. PLoS One 2015; 10:e0121691. [PMID: 25830638 PMCID: PMC4382148 DOI: 10.1371/journal.pone.0121691] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Accepted: 02/14/2015] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Pulmonary injury is the main cause of death in acute paraquat (PQ) poisoning. However, whether quantitative lung computed tomography (CT) can be useful in predicting the outcome of PQ poisoning remains unknown. We aimed to identify early findings of quantitative lung CT as predictors of outcome in acute PQ poisoning. METHODS Lung CT scanning (64-slide) and quantitative CT lesions were prospectively measured for patients after PQ intoxication within 5 days. The study outcome was mortality during 90 days follow-up. Survival curves were derived by the Kaplan-Meier method, and mortality risk factors were analyzed by the forward stepwise Cox regression analysis. RESULTS Of 97 patients, 41 (42.3%) died. Among the eight different types of lung CT findings which appeared in the first 5-day of PQ intoxication, four ones discriminated between survivors and non-survivors including ground glass opacity (GGO), consolidation, pneumomediastinum and "no obvious lesion". With a cutoff value of 10.8%, sensitivity of 85.4% and specificity of 89.3%, GGO volume ratio is better than adopted outcome indicators in predicting mortality, such as estimated amount of PQ ingestion, plasma or urine PQ concentration, acute physiology and chronic health evaluation (APACHE) II and sequential organ failure assessment (SOFA) scores. GGO volume ratios above 10.8% were associated with increased mortality (hazard ratio, 5.82; 95% confidence interval, 4.77-7.09; P < 0.001). CONCLUSIONS The volume ratio of GGO exceeding 10.8% is a novel, reliable and independent predictors of outcome in acute PQ poisoning.
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Mohamed Hoesein FAA, de Jong PA, Lammers JWJ, Mali WPTM, Schmidt M, de Koning HJ, van der Aalst C, Oudkerk M, Vliegenthart R, Groen HJM, van Ginneken B, van Rikxoort EM, Zanen P. Airway wall thickness associated with forced expiratory volume in 1 second decline and development of airflow limitation. Eur Respir J 2015; 45:644-51. [PMID: 25614166 DOI: 10.1183/09031936.00020714] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Airway wall thickness and emphysema contribute to airflow limitation. We examined their association with lung function decline and development of airflow limitation in 2021 male smokers with and without airflow limitation. Airway wall thickness and emphysema were quantified on chest computed tomography and expressed as the square root of wall area of a 10-mm lumen perimeter (Pi10) and the 15th percentile method (Perc15), respectively. Baseline and follow-up (median (interquartile range) 3 (2.9-3.1) years) spirometry was available. Pi10 and Perc15 correlated with baseline forced expiratory volume in 1 s (FEV1) (r= -0.49 and 0.11, respectively (p<0.001)). Multiple linear regression showed that Pi10 and Perc15 at baseline were associated with a lower FEV1 after follow-up (p<0.05). For each sd increase in Pi10 and decrease in Perc15 the FEV1 decreased by 20 mL and 30.2 mL, respectively. The odds ratio for developing airflow limitation after 3 years was 2.45 for a 1-mm higher Pi10 and 1.46 for a 10-HU lower Perc15 (p<0.001). A greater degree of airway wall thickness and emphysema was associated with a higher FEV1 decline and development of airflow limitation after 3 years of follow-up.
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Affiliation(s)
| | - Pim A de Jong
- Dept of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jan-Willem J Lammers
- Dept of Respiratory Medicine, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willem P T M Mali
- Dept of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Michael Schmidt
- Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany
| | | | | | - Matthijs Oudkerk
- Center for Medical Imaging - North East Netherlands, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Dept of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Harry J M Groen
- Dept of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands GRIAD Research Institute, University Medical Center Groningen, Groningen, The Netherlands
| | - Bram van Ginneken
- Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Eva M van Rikxoort
- Fraunhofer MEVIS, Institute for Medical Image Computing, Bremen, Germany Diagnostic Image Analysis Group, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Pieter Zanen
- Dept of Respiratory Medicine, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
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Coxson HO, Leipsic J, Parraga G, Sin DD. Using Pulmonary Imaging to Move Chronic Obstructive Pulmonary Disease beyond FEV1. Am J Respir Crit Care Med 2014; 190:135-44. [DOI: 10.1164/rccm.201402-0256pp] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Kessler LG, Barnhart HX, Buckler AJ, Choudhury KR, Kondratovich MV, Toledano A, Guimaraes AR, Filice R, Zhang Z, Sullivan DC. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 2014; 24:9-26. [PMID: 24919826 DOI: 10.1177/0962280214537333] [Citation(s) in RCA: 196] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The development and implementation of quantitative imaging biomarkers has been hampered by the inconsistent and often incorrect use of terminology related to these markers. Sponsored by the Radiological Society of North America, an interdisciplinary group of radiologists, statisticians, physicists, and other researchers worked to develop a comprehensive terminology to serve as a foundation for quantitative imaging biomarker claims. Where possible, this working group adapted existing definitions derived from national or international standards bodies rather than invent new definitions for these terms. This terminology also serves as a foundation for the design of studies that evaluate the technical performance of quantitative imaging biomarkers and for studies of algorithms that generate the quantitative imaging biomarkers from clinical scans. This paper provides examples of research studies and quantitative imaging biomarker claims that use terminology consistent with these definitions as well as examples of the rampant confusion in this emerging field. We provide recommendations for appropriate use of quantitative imaging biomarker terminological concepts. It is hoped that this document will assist researchers and regulatory reviewers who examine quantitative imaging biomarkers and will also inform regulatory guidance. More consistent and correct use of terminology could advance regulatory science, improve clinical research, and provide better care for patients who undergo imaging studies.
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Affiliation(s)
| | | | | | | | | | | | | | - Ross Filice
- Food and Drug Administration, Silver Spring, MD, USA
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Affiliation(s)
- Miranda Kirby
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
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17
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Abstract
The goal of quantitative analysis of computed tomography (CT) scans is to understand the anatomic structure that is responsible for the physiological function of the lung. The gold standard for structural analysis requires the examination of tissue, which is not practical in most studies. Quantitative CT allows valuable information on lung structure to be obtained without removal of tissue from the body, thereby aiding longitudinal studies on chronic lung diseases. This review briefly discusses CT analysis of the lung and some of the sources of variation that can cause differences in the CT metrics used for analysis of lung disease. Although there are many sources of variation, this review will show that, if the study is properly designed to take into account these variations and if the CT scanner is properly calibrated, valuable information can be obtained from CT scans that should allow us to study the pathogenesis of lung disease and the effect of treatment.
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Pike D, Lindenmaier TJ, Sin DD, Parraga G. Imaging evidence of the relationship between atherosclerosis and chronic obstructive pulmonary disease. ACTA ACUST UNITED AC 2014. [DOI: 10.2217/iim.13.70] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Multidetector Computed Tomographic Imaging in Chronic Obstructive Pulmonary Disease. Radiol Clin North Am 2014; 52:137-54. [DOI: 10.1016/j.rcl.2013.09.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Diaz AA, Han MK, Come CE, San José Estépar R, Ross JC, Kim V, Dransfield MT, Curran-Everett D, Schroeder JD, Lynch DA, Tschirren J, Silverman EK, Washko GR. Effect of emphysema on CT scan measures of airway dimensions in smokers. Chest 2013; 143:687-693. [PMID: 23460155 DOI: 10.1378/chest.12-0039] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
BACKGROUND In CT scans of smokers with COPD, the subsegmental airway wall area percent (WA%) is greater and more strongly correlated with FEV1 % predicted than WA% obtained in the segmental airways. Because emphysema is linked to loss of airway tethering and may limit airway expansion, increases in WA% may be related to emphysema and not solely to remodeling. We aimed to first determine whether the stronger association of subsegmental vs segmental WA% with FEV1 % predicted is mitigated by emphysema and, second, to assess the relationships among emphysema, WA%, and total bronchial area (TBA). METHODS We analyzed CT scan segmental and subsegmental WA% (WA% = 100 × wall area/TBA) of six bronchial paths and corresponding lobar emphysema, lung function, and clinical data in 983 smokers with COPD. RESULTS Compared with segmental WA%, the subsegmental WA% had a greater effect on FEV1% predicted (-0.8% to -1.7% vs -1.9% to -2.6% per 1-unit increase in WA%, respectively; P < .05 for most bronchial paths). After adjusting for emphysema, the association between subsegmental WA% and FEV1 % predicted was weakened in two bronchial paths. Increases in WA% between bronchial segments correlated directly with emphysema in all bronchial paths (P < .05). In multivariate regression models, emphysema was directly related to subsegmental WA% in most bronchial paths and inversely related to subsegmental TBA in all bronchial paths. CONCLUSION The greater effect of subsegmental WA% on airflow obstruction is mitigated by emphysema. Part of the emphysema effect might be due to loss of airway tethering, leading to a reduction in TBA and an increase in WA%.
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Affiliation(s)
- Alejandro A Diaz
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Department of Pulmonary Diseases, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - MeiLan K Han
- University of Michigan School of Medicine, Ann Arbor, MI
| | - Carolyn E Come
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Raúl San José Estépar
- Surgical Planning Laboratory, Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA
| | - James C Ross
- Surgical Planning Laboratory, Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA
| | - Victor Kim
- School of Medicine, Temple University, Philadelphia, PA
| | - Mark T Dransfield
- Division of Pulmonary, Allergy, and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, AL
| | - Douglas Curran-Everett
- Division of Biostatistics and Bioinformatics, National Jewish Health, Denver, CO; Department of Biostatistics and Informatics, Colorado School of Public Health, Denver, CO
| | - Joyce D Schroeder
- Division of Radiology, National Jewish Health, University of Colorado, School of Medicine, Denver, CO
| | - David A Lynch
- Division of Radiology, National Jewish Health, University of Colorado, School of Medicine, Denver, CO
| | | | - Edwin K Silverman
- Channing Laboratory (Dr Silverman), Brigham and Women's Hospital, Boston, MA
| | - George R Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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Mohamed Hoesein FAA, Zanen P, Boezen HM, Groen HJM, van Ginneken B, de Jong PA, Postma DS, Lammers JWJ. Lung function decline in male heavy smokers relates to baseline airflow obstruction severity. Chest 2013; 142:1530-1538. [PMID: 22722231 DOI: 10.1378/chest.11-2837] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
BACKGROUND Recent evidence indicates that the rate of lung function decline is steepest in mild COPD and slower in moderate to severe COPD. The current study assessed whether lung function decline relates to baseline airflow obstruction severity in male heavy smokers. METHODS In total, 2,003 male smokers with a mean (SD) age of 59.8 (5.3) years underwent pulmonary function testing at baseline and after 3-year follow-up. Participants were classified by entry FEV1/FVC as follows: group 1, >70%; group 2, <70%, but greater than lower limit of normal (LLN); and group 3, less than LLN. Differences in lung function decline among the groups were assessed using multiple regression after adjustment for pack-years, smoking status (current or former smoker), presence or absence of mucus production, medical center, height, age, CT scan-derived emphysema severity (15th percentile), observation time (years in study), and the baseline values. RESULTS Over 3 years, the mean (SD) FEV₁/FVC, FEV₁, and maximum expiratory flow at 50% of FVC decreases in group 1 were 3.1% (1), 0.21 L (0.07), and 0.40 L/s (0.26), respectively. In group 3, these decreases were 2.4% (1.1), 0.15 L (0.08), and 0.06 L/s (0.19), respectively. All lung function parameters showed the greatest decline in group 1 (P < .001). CONCLUSIONS Diagnosing COPD based on the presence of more severe airflow obstruction (as defined by FEV₁/FVC less than LLN) means that, at the time of such a diagnosis, subjects had passed the phase of strong lung-function decline. TRIAL REGISTRY ISRCTN Register; No.: ISRCTN63545820; URL: www.trialregister.nl
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Affiliation(s)
| | - Pieter Zanen
- Division of Heart and Lungs, Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht.
| | - H Marike Boezen
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen
| | - Harry J M Groen
- Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen
| | - Bram van Ginneken
- Image Sciences Institute, Department of Radiology, University Medical Center Utrecht, Utrecht; Diagnostic Image Analysis Group, Department of Radiology, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht
| | - Dirkje S Postma
- Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen
| | - Jan-Willem J Lammers
- Division of Heart and Lungs, Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht
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Washko GR, Parraga G, Coxson HO. Quantitative pulmonary imaging using computed tomography and magnetic resonance imaging. Respirology 2012; 17:432-44. [PMID: 22142490 DOI: 10.1111/j.1440-1843.2011.02117.x] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Measurements of lung function, including spirometry and body plethesmography, are easy to perform and are the current clinical standard for assessing disease severity. However, these lung functional techniques do not adequately explain the observed variability in clinical manifestations of disease and offer little insight into the relationship of lung structure and function. Lung imaging and the image-based assessment of lung disease has matured to the extent that it is common for clinical, epidemiologic and genetic investigation to have a component dedicated to image analysis. There are several exciting imaging modalities currently being used for the non-invasive study of lung anatomy and function. In this review, we will focus on two of them; X-ray computed tomography and magnetic resonance imaging. Following a brief introduction of each method, we detail some of the most recent work being done to characterize smoking-related lung disease and the clinical applications of such knowledge.
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Affiliation(s)
- George R Washko
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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23
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Xie X, de Jong PA, Oudkerk M, Wang Y, Ten Hacken NHT, Miao J, Zhang G, de Bock GH, Vliegenthart R. Morphological measurements in computed tomography correlate with airflow obstruction in chronic obstructive pulmonary disease: systematic review and meta-analysis. Eur Radiol 2012; 22:2085-93. [PMID: 22699870 PMCID: PMC3431473 DOI: 10.1007/s00330-012-2480-8] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Revised: 03/20/2012] [Accepted: 03/26/2012] [Indexed: 01/08/2023]
Abstract
Objectives To determine the correlation between CT measurements of emphysema or peripheral airways and airflow obstruction in chronic obstructive pulmonary disease (COPD). Methods PubMed, Embase and Web of Knowledge were searched from 1976 to 2011. Two reviewers independently screened 1,763 citations to identify articles that correlated CT measurements to airflow obstruction parameters of the pulmonary function test in COPD patients, rated study quality and extracted information. Three CT measurements were accessed: lung attenuation area percentage < -950 Hounsfield units, mean lung density and airway wall area percentage. Two airflow obstruction parameters were accessed: forced expiratory volume in the first second as percentage from predicted (FEV1 %pred) and FEV1 divided by the forced volume vital capacity. Results Seventy-nine articles (9,559 participants) were included in the systematic review, demonstrating different methodologies, measurements and CT airflow obstruction correlations. There were 15 high-quality articles (2,095 participants) in the meta-analysis. The absolute pooled correlation coefficients ranged from 0.48 (95 % CI, 0.40 to 0.54) to 0.65 (0.58 to 0.71) for inspiratory CT and 0.64 (0.53 to 0.72) to 0.73 (0.63 to 0.80) for expiratory CT. Conclusions CT measurements of emphysema or peripheral airways are significantly related to airflow obstruction in COPD patients. CT provides a morphological method to investigate airway obstruction in COPD. Key Points • Computed tomography is widely performed in patients with chronic obstructive pulmonary disease (COPD) • CT provides quantitative morphological methods to investigate airflow obstruction in COPD • CT measurements correlate significantly with the degree of airflow obstruction in COPD • Expiratory CT measurements correlate more strongly with airflow obstruction than inspiratory CT • Low-dose CT decreases the radiation dose for diagnosis and quantitative emphysema evaluation Electronic supplementary material The online version of this article (doi:10.1007/s00330-012-2480-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xueqian Xie
- Center for Medical Imaging-North East Netherlands (CMI-NEN), Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 30.001, 9700, RB, Groningen, The Netherlands
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Diciotti S, Sverzellati N, Kauczor HU, Lombardo S, Falchini M, Favilli G, Macconi L, Kuhnigk JM, Marchianò A, Pastorino U, Zompatori M, Mascalchi M. Defining the intra-subject variability of whole-lung CT densitometry in two lung cancer screening trials. Acad Radiol 2011; 18:1403-11. [PMID: 21971258 DOI: 10.1016/j.acra.2011.08.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2011] [Revised: 07/26/2011] [Accepted: 08/01/2011] [Indexed: 11/18/2022]
Abstract
RATIONALE AND OBJECTIVES To define a statistically based variation of individual whole-lung densitometry above which a real increase of pulmonary extent can be suspected in lung cancer screening trials. MATERIALS AND METHODS Baseline and 3-month follow-up low-dose computed tomography (LDCT) examinations of 131 smokers or former smokers recruited in the ITALUNG (32 subjects) and MILD (99 subjects) trials were compared using for each data set two different image processing tools for whole-lung densitometry. Both trials were approved by institutional review boards, and written informed consent was obtained from all participants. Assuming that no change of emphysema extent can occur in a 3-month interval, the Bland and Altman method was used to assess the agreement between baseline and follow-up LDCT examinations for lung volume, 15th percentile (Perc15) of lung density and Perc15 corrected for lung volume by application of a linear detrend on log-transformed data. RESULTS Similar results were obtained in each data set using two different image processing tools. In the ITALUNG cohort the 95% limits of agreement (LoA) interval of volume corrected Perc15 was -9.7 to 10.7% using image processing method 1 and -10.3 to 11.5% using image processing method 2. In the MILD cohort, the 95% LoA interval of volume corrected Perc15 was -14.7 to 17.3% with both image processing methods. CONCLUSION In the two considered lung cancer screening settings a range of 9.7-14.7% decrease of volume corrected Perc15 represents a statistically defined threshold to suspect a real increase of emphysema extent in serial LDCT examinations.
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Affiliation(s)
- Stefano Diciotti
- Computational Biomedical Imaging Laboratory, Radiodiagnostic Section, Department of Clinical Physiopathology, University of Florence, Florence, Italy
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Shimizu K, Hasegawa M, Makita H, Nasuhara Y, Konno S, Nishimura M. Comparison of airway remodelling assessed by computed tomography in asthma and COPD. Respir Med 2011; 105:1275-83. [PMID: 21646007 DOI: 10.1016/j.rmed.2011.04.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2011] [Revised: 03/18/2011] [Accepted: 04/12/2011] [Indexed: 01/28/2023]
Abstract
BACKGROUND Few studies have directly compared airway remodelling assessed by computed tomography (CT) between asthma and chronic obstructive pulmonary disease (COPD). The present study was conducted to determine whether there are any differences between the two diseases with similar levels of airflow limitation under clinically stable conditions. METHODS Subjects included older male asthmatic patients (n = 19) showing FEV(1)/FVC <70% with smoking history less than 5-pack/year. Age- and sex-matched COPD patients (n = 28) who demonstrated similar airflow limitation as asthmatic patients and age-matched healthy non-smokers (n = 13) were recruited. Using proprietary software, eight airways were selected in the right lung, and wall area percent (WA%) and airway luminal area (Ai) were measured at the mid-portion of the 3rd to 6th generation of each airway. For comparison, the average of eight measurements per generation was recorded. RESULTS FEV(1)% predicted and FEV(1)/FVC was similar between asthma and COPD (82.3 ± 3.3% vs. 77.6 ± 1.8% and 57.7 ± 1.6% vs. 57.9 ± 1.4%). At any generation, WA% was larger and Ai was smaller in asthma, both followed by COPD and then controls. Significant differences were observed between asthma and controls in WA% of the 3rd to 5th generation and Ai of any generation, while no differences were seen between COPD and controls. There were significant differences in Ai of any generation between asthma and COPD. CONCLUSIONS Airway remodelling assessed by CT is more prominent in asthma compared with age- and sex-matched COPD subjects in the 3rd- to 6th generation airways when airflow limitations were similar under stable clinical conditions.
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Affiliation(s)
- Kaoruko Shimizu
- First Department of Medicine, Hokkaido University School of Medicine, N-15 W-7, Kita-ku, Sapporo 060-8638, Japan
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Grydeland TB, Thorsen E, Dirksen A, Jensen R, Coxson HO, Pillai SG, Sharma S, Eide GE, Gulsvik A, Bakke PS. Quantitative CT measures of emphysema and airway wall thickness are related to D(L)CO. Respir Med 2010; 105:343-51. [PMID: 21074394 DOI: 10.1016/j.rmed.2010.10.018] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2010] [Revised: 10/03/2010] [Accepted: 10/18/2010] [Indexed: 11/28/2022]
Abstract
UNLABELLED There is limited knowledge on the relationship between diffusing capacity of the lung for carbon monoxide (D(L)CO) and quantitative computed tomography (CT) measures of emphysema and airway wall thickness. STUDY QUESTION What is the relationship between D(L)CO and the quantitative CT measures of emphysema and airway wall thickness in subjects with and without COPD? METHODS We included 288 COPD subjects (70% men) and 425 non-COPD subjects (54% men). All subjects were current or ex-smokers older than 40 years and all subjects underwent spirometry, diffusing capacity tests and CT examination. Quantitative CT measures included % low attenuation areas < -950 HU (%LAA) and standardized airway wall thickness (AWT-Pi10). RESULTS Multiple linear regression analyses showed significant associations between D(L)CO and both %LAA and AWT-Pi10 in the COPD group. The adjusted regression coefficients (SE) for D(L)CO (mmol min(-1) kPa(-1)) were -1.15 (0.11) per 10% increase in %LAA and 0.08 (0.03) per 0.1 mm increase in AWT-Pi10, and the models' adjusted R(2) was 0.65 and 0.49, respectively. CONCLUSIONS CT measured emphysema explains a large fraction of the variation of D(L)CO among COPD subjects, and more so in men. Airway wall thickness is also significantly associated with D(L)CO, but explains a much smaller fraction of the variation.
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Affiliation(s)
- Thomas B Grydeland
- Department of Thoracic Medicine, Haukeland University Hospital, Jonas Lies v 65, N-5021 Bergen, Norway.
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Grydeland TB, Dirksen A, Coxson HO, Eagan TML, Thorsen E, Pillai SG, Sharma S, Eide GE, Gulsvik A, Bakke PS. Quantitative computed tomography measures of emphysema and airway wall thickness are related to respiratory symptoms. Am J Respir Crit Care Med 2009; 181:353-9. [PMID: 19926869 DOI: 10.1164/rccm.200907-1008oc] [Citation(s) in RCA: 170] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
RATIONALE There is limited knowledge about the relationship between respiratory symptoms and quantitative high-resolution computed tomography measures of emphysema and airway wall thickness. OBJECTIVES To describe the ability of these measures of emphysema and airway wall thickness to predict respiratory symptoms in subjects with and without chronic obstructive pulmonary disease (COPD). METHODS We included 463 subjects with chronic obstructive pulmonary disease (COPD) (65% men) and 488 subjects without COPD (53% men). All subjects were current or ex-smokers older than 40 years. They underwent spirometry and high-resolution computed tomography examination, and completed an American Thoracic Society questionnaire on respiratory symptoms. MEASUREMENTS AND MAIN RESULTS Median (25th percentile, 75th percentile) percent low-attenuation areas less than -950 Hounsfield units (%LAA) was 7.0 (2.2, 17.8) in subjects with COPD and 0.5 (0.2, 1.3) in subjects without COPD. Mean (SD) standardized airway wall thickness (AWT) at an internal perimeter of 10 mm (AWT-Pi10) was 4.94 (0.33) mm in subjects with COPD and 4.77 (0.29) in subjects without COPD. Both %LAA and AWT-Pi10 were independently and significantly related to the level of dyspnea among subjects with COPD, even after adjustments for percent predicted FEV(1). AWT-Pi10 was significantly related to cough and wheezing in subjects with COPD, and to wheezing in subjects without COPD. Odds ratios (95% confidence intervals) for increased dyspnea in subjects with COPD and in subjects without COPD were 1.9 (1.5-2.3) and 1.9 (0.6-6.6) per 10% increase in %LAA, and 1.07 (1.01-1.14) and 1.11 (0.99-1.24) per 0.1-mm increase in AWT-Pi10, respectively. CONCLUSIONS Quantitative computed tomography assessment of the lung parenchyma and airways may be used to explain the presence of respiratory symptoms beyond the information offered by spirometry.
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Affiliation(s)
- Thomas B Grydeland
- Department of Thoracic Medicine, Haukeland University Hospital, N-5021 Bergen, Norway.
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Coxson HO, Mayo J, Lam S, Santyr G, Parraga G, Sin DD. New and current clinical imaging techniques to study chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2009; 180:588-97. [PMID: 19608719 DOI: 10.1164/rccm.200901-0159pp] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characterized by both small airway and parenchymal abnormalities. There is increasing evidence to suggest that these two morphologic phenotypes, although related, may have different clinical presentations, prognosis, and therapeutic responses to medications. With the advent of novel imaging modalities, it is now possible to evaluate these two morphologic phenotypes in large clinical studies using noninvasive or minimally invasive methods such as computed tomography (CT), magnetic resonance imaging (MRI), and optical coherence tomography (OCT). In this article, we provide an overview of these imaging modalities in the context of COPD and discuss their strengths as well as their limitations for providing quantitative COPD phenotypes.
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Affiliation(s)
- Harvey O Coxson
- Providence Heart and Lung Institute, St. Paul's Hospital, Vancouver, British Columbia, Canada
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